Text Generation
Transformers
TensorBoard
Safetensors
English
gemma
Trained with AutoTrain
conversational
text-generation-inference
Instructions to use JesseWeigel/autotrain-5dehv-0ke56 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JesseWeigel/autotrain-5dehv-0ke56 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="JesseWeigel/autotrain-5dehv-0ke56") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("JesseWeigel/autotrain-5dehv-0ke56") model = AutoModelForCausalLM.from_pretrained("JesseWeigel/autotrain-5dehv-0ke56", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use JesseWeigel/autotrain-5dehv-0ke56 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JesseWeigel/autotrain-5dehv-0ke56" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JesseWeigel/autotrain-5dehv-0ke56", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/JesseWeigel/autotrain-5dehv-0ke56
- SGLang
How to use JesseWeigel/autotrain-5dehv-0ke56 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "JesseWeigel/autotrain-5dehv-0ke56" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JesseWeigel/autotrain-5dehv-0ke56", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "JesseWeigel/autotrain-5dehv-0ke56" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JesseWeigel/autotrain-5dehv-0ke56", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use JesseWeigel/autotrain-5dehv-0ke56 with Docker Model Runner:
docker model run hf.co/JesseWeigel/autotrain-5dehv-0ke56
| from typing import Dict, List, Any | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline | |
| import torch | |
| from peft import PeftModel | |
| import json | |
| import os | |
| class EndpointHandler(): | |
| def __init__(self, path=""): | |
| base_model_path = json.load(open(os.path.join(path, "training_params.json")))["model"] | |
| model = AutoModelForCausalLM.from_pretrained( | |
| base_model_path, | |
| torch_dtype=torch.float16, | |
| low_cpu_mem_usage=True, | |
| trust_remote_code=True, | |
| device_map="auto", | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True) | |
| model.resize_token_embeddings(len(tokenizer)) | |
| model = PeftModel.from_pretrained(model, path) | |
| model = model.merge_and_unload() | |
| self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer) | |
| def __call__(self, data: Any) -> List[List[Dict[str, float]]]: | |
| inputs = data.pop("inputs", data) | |
| parameters = data.pop("parameters", None) | |
| if parameters is not None: | |
| prediction = self.pipeline(inputs, **parameters) | |
| else: | |
| prediction = self.pipeline(inputs) | |
| return prediction |